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 "cells": [
  {
   "source": [
    "# 心电分类模型"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模块\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_filepath = \"../data/numpy_data/\"\n",
    "\n",
    "X_train = np.load(data_filepath + 'X_train.npy')\n",
    "y_train = np.load(data_filepath + 'y_train.npy', allow_pickle=True)\n",
    "X_test = np.load(data_filepath + 'X_test.npy')\n",
    "y_test = np.load(data_filepath + 'y_test.npy', allow_pickle=True)\n",
    "\n",
    "# reshape y_train, y_test\n",
    "y_train = y_train.reshape(len(y_train), 1)\n",
    "y_test = y_test.reshape(len(y_test), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffle_train_test(X_train, y_train, X_test, y_test):\n",
    "    shuffled_indices_x = np.random.permutation(len(y_train))\n",
    "    shuffled_indices_y = np.random.permutation(len(y_test))\n",
    "\n",
    "    return X_train[shuffled_indices_x], y_train[shuffled_indices_x], X_test[shuffled_indices_y], y_test[shuffled_indices_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 打乱数据\n",
    "X_train, y_train, X_test, y_test = shuffle_train_test(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "source": [
    "## 查看数据并调整数据格式"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 查看输入x和标签的维度是否匹配"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(16966, 500, 12)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(16966, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1901, 500, 12)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "source": [
    "y_test.shape"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 将y映射为数组"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "num2class = np.array(['NORM', 'MI', 'STTC', 'CD', 'HYP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "np.where(num2class == 'STTC')[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in range(len(y_train)):\n",
    "    y_train[index] = np.where(num2class == y_train[index][0][0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index in range(len(y_test)):\n",
    "    y_test[index] = np.where(num2class == y_test[index][0][0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape(1, -1)[0].astype('uint8')\n",
    "y_test = y_test.reshape(1, -1)[0].astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([3, 0, 0, ..., 4, 1, 0], dtype=uint8)"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "source": [
    "## 数据集过采样\n",
    "\n",
    "这里的心电数据样本存在样本不均衡的问题，所有的分类中正样本比例均显著低于负样本比例，如果采用欠采样的话会使训练集丢失部分数据，而过采样会导致一个数据点在高维空间出现多次，增加过拟合风险，很多研究通过在过采样中加入少量随机噪声来减少这类风险\n",
    "\n",
    "这里需要安装`imblearn`包，使用以下的命令行安装\n",
    "\n",
    "```shell\n",
    "pip install imbalanced-learn\n",
    "```"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversampler = SMOTE(random_state=42)\n",
    "\n",
    "os_features, os_label = oversampler.fit_sample(X_train.reshape(len(X_train), 6000), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(36080, 6000)"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "os_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(36080,)"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "os_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 查看数据分布\n",
    "plt.figure()\n",
    "plt.hist(os_label)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## PCA降维"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "source": [
    "### 训练集PCA降维"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PCA(n_components=240)"
      ]
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "pca_train = PCA(n_components=240)\n",
    "pca_train.fit(os_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca = pca_train.fit_transform(os_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9908215246322652"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "pca_train.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(36080, 240)"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "X_train_pca.shape"
   ]
  },
  {
   "source": [
    "### 测试集PCA降维"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_flatten = X_test.reshape(len(X_test), 6000)\n",
    "pca_test = PCA(n_components=240)\n",
    "pca_test = PCA(X_test_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pca = pca_train.fit_transform(X_test_flatten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(1901, 240)"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "X_test_pca.shape"
   ]
  },
  {
   "source": [
    "## 训练模型 "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "---train class is STTC---\n"
     ]
    }
   ],
   "source": [
    "train_class = 2\n",
    "print(\"---train class is %s---\"%(num2class[train_class]))\n",
    "y_train_norm = (os_label == train_class)\n",
    "y_test_norm = (y_test == train_class)\n",
    "\n",
    "# y_train_mi = (os_label == 1).reshape(1, -1)[0]\n",
    "# y_test_mi = (y_test == 1).reshape(1, -1)[0]\n",
    "\n",
    "# y_train_sttc = (os_label == 2).reshape(1, -1)[0]\n",
    "# y_test_sttc = (y_test == 2).reshape(1, -1)[0]\n",
    "\n",
    "# y_train_cd = (os_label == 3).reshape(1, -1)[0]\n",
    "# y_test_cd = (y_test == 3).reshape(1, -1)[0]\n",
    "\n",
    "# y_train_hyp = (os_label == 4).reshape(1, -1)[0]\n",
    "# y_test_hyp = (y_test == 4).reshape(1, -1)[0]"
   ]
  },
  {
   "source": [
    "### 二分类模型"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "SGDClassifier(random_state=42)"
      ]
     },
     "metadata": {},
     "execution_count": 133
    }
   ],
   "source": [
    "# sgd_clf = Pipeline([\n",
    "#     (\"scaler\", StandardScaler()),\n",
    "#     (\"sgd_clf\", SGDClassifier(random_state=42))\n",
    "# ])\n",
    "sgd_clf = SGDClassifier(random_state=42)\n",
    "sgd_clf.fit(X_train_pca, y_train_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False,  True, False, False,\n",
       "       False])"
      ]
     },
     "metadata": {},
     "execution_count": 134
    }
   ],
   "source": [
    "y_predict_sgd = sgd_clf.predict(X_test_pca)\n",
    "y_predict_sgd[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False,  True,  True, False,\n",
       "       False])"
      ]
     },
     "metadata": {},
     "execution_count": 135
    }
   ],
   "source": [
    "y_test_norm[0:10]"
   ]
  },
  {
   "source": [
    "#### K折交叉验证"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.base import clone\n",
    "\n",
    "def k_fold_val(classifier, k_fold=5):\n",
    "    skfolds = StratifiedKFold(n_splits=5, random_state=42, shuffle=True)\n",
    "    fold = 1\n",
    "    total_accu = 0\n",
    "    for train_index, test_index in skfolds.split(X_train_pca, y_train_norm):\n",
    "        clone_clf = clone(classifier)\n",
    "        X_train_folds = X_train_pca[train_index]\n",
    "        y_train_folds = y_train_norm[train_index]\n",
    "        X_test_fold = X_train_pca[test_index]\n",
    "        y_test_fold = y_train_norm[test_index]\n",
    "        clone_clf.fit(X_train_folds, y_train_folds)\n",
    "        y_pred = clone_clf.predict(X_test_fold)\n",
    "        n_correct = sum(y_pred == y_test_fold)\n",
    "        print(\"Fold %d accuracy: %f\"%(fold, n_correct / len(y_pred)))\n",
    "        fold += 1\n",
    "        total_accu += n_correct / len(y_pred)\n",
    "    print(\"\\nTotal Accuracy: %f\" % (total_accu/k_fold))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fold 1 accuracy: 0.783814\n",
      "Fold 2 accuracy: 0.784091\n",
      "Fold 3 accuracy: 0.778686\n",
      "Fold 4 accuracy: 0.777578\n",
      "Fold 5 accuracy: 0.789911\n",
      "\n",
      "Total Accuracy: 0.782816\n"
     ]
    }
   ],
   "source": [
    "k_fold_val(sgd_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0.78187361, 0.78395233, 0.78727827, 0.77896341, 0.62444568])"
      ]
     },
     "metadata": {},
     "execution_count": 139
    }
   ],
   "source": [
    "cross_val_score(sgd_clf, X_train_pca, y_train_norm, cv=5, scoring=\"accuracy\")"
   ]
  },
  {
   "source": [
    "#### 查看混淆矩阵"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix,precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[26350,  2514],\n",
       "       [ 6459,   757]])"
      ]
     },
     "metadata": {},
     "execution_count": 141
    }
   ],
   "source": [
    "y_train_pred_sgd = cross_val_predict(sgd_clf, X_train_pca, y_train_norm, cv=5)\n",
    "confusion_matrix(y_train_norm, y_train_pred_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.23142769795169674"
      ]
     },
     "metadata": {},
     "execution_count": 142
    }
   ],
   "source": [
    "precision_score(y_train_norm, y_train_pred_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.10490576496674058"
      ]
     },
     "metadata": {},
     "execution_count": 143
    }
   ],
   "source": [
    "recall_score(y_train_norm, y_train_pred_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.1443692190330886"
      ]
     },
     "metadata": {},
     "execution_count": 144
    }
   ],
   "source": [
    "f1_score(y_train_norm, y_train_pred_sgd)"
   ]
  },
  {
   "source": [
    "#### ROC曲线"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "y_scores_sgd = cross_val_predict(sgd_clf, X_train_pca, y_train_norm, cv=5,\n",
    "method=\"decision_function\")\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_train_norm, y_scores_sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(fpr, tpr, label=None):\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.45509206759369425"
      ]
     },
     "metadata": {},
     "execution_count": 148
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_train_norm, y_scores_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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140.108611 \nL 113.081864 139.971643 \nL 113.124043 139.889462 \nL 113.187312 139.670312 \nL 120.273383 135.259931 \nL 120.273383 135.232538 \nL 120.442099 135.17775 \nL 120.526457 135.040782 \nL 120.705718 134.958601 \nL 120.811165 134.712058 \nL 120.958792 134.602483 \nL 121.053694 134.492909 \nL 121.148597 134.383334 \nL 121.2435 134.246366 \nL 121.412216 134.136791 \nL 121.486029 133.945035 \nL 121.844551 133.835461 \nL 121.949998 133.643705 \nL 122.118714 133.53413 \nL 122.160893 133.369768 \nL 122.319064 133.287587 \nL 122.424512 133.150619 \nL 122.50887 133.041044 \nL 122.551049 132.931469 \nL 122.783033 132.849288 \nL 122.888481 132.684926 \nL 123.13101 132.602745 \nL 123.225913 132.41099 \nL 123.33136 132.301415 \nL 123.394629 132.19184 \nL 123.637158 132.082266 \nL 123.679337 132.000085 \nL 123.858598 131.89051 \nL 123.942956 131.835723 \nL 124.058948 131.726148 \nL 124.164395 131.479605 \nL 124.322567 131.37003 \nL 124.417469 131.260456 \nL 124.533462 131.150881 \nL 124.586185 131.013913 \nL 124.849804 130.904338 \nL 124.902528 130.794763 \nL 125.092333 130.685188 \nL 125.187236 130.520826 \nL 125.324318 130.438645 \nL 125.355952 130.356464 \nL 125.566847 130.24689 \nL 125.672295 130.109921 \nL 125.8621 130.02774 \nL 125.967547 129.699016 \nL 126.051905 129.644229 \nL 126.157353 129.452473 \nL 126.368248 129.342899 \nL 126.463151 129.20593 \nL 126.610777 129.096356 \nL 126.631867 129.014175 \nL 126.937664 128.9046 \nL 127.032567 128.712844 \nL 127.222373 128.60327 \nL 127.285641 128.356726 \nL 127.443812 128.247152 \nL 127.538715 128.137577 \nL 127.686342 128.028002 \nL 127.781244 127.808853 \nL 128.118676 127.699278 \nL 128.224124 127.534916 \nL 128.350661 127.425342 \nL 128.403385 127.343161 \nL 128.59319 127.233586 \nL 128.667003 127.151405 \nL 128.962256 127.04183 \nL 129.046614 126.795287 \nL 129.21533 126.685713 \nL 129.299688 126.521351 \nL 129.478949 126.411776 \nL 129.552762 126.329595 \nL 129.647665 126.247414 \nL 129.753112 126.055658 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116.330905 \nL 142.122103 116.139149 \nL 142.259185 116.029575 \nL 142.364632 115.865213 \nL 142.427901 115.755638 \nL 142.44899 115.591276 \nL 142.733698 115.481701 \nL 142.807512 115.235158 \nL 142.944593 115.125584 \nL 143.039496 114.933828 \nL 143.166033 114.824253 \nL 143.229302 114.605104 \nL 143.630002 114.495529 \nL 143.73545 114.331167 \nL 143.809263 114.248986 \nL 143.861987 114.139411 \nL 144.083426 114.029837 \nL 144.188874 113.947656 \nL 144.262687 113.865475 \nL 144.304866 113.7559 \nL 144.547395 113.646325 \nL 144.610664 113.45457 \nL 144.768835 113.372389 \nL 144.853193 113.23542 \nL 144.937551 113.153239 \nL 145.00082 112.851909 \nL 145.274983 112.742334 \nL 145.38043 112.605366 \nL 145.549146 112.495791 \nL 145.62296 112.41361 \nL 145.728407 112.331429 \nL 145.791676 112.167067 \nL 145.939302 112.057492 \nL 146.02366 111.89313 \nL 146.318913 111.783556 \nL 146.413816 111.646587 \nL 146.582532 111.537013 \nL 146.677435 111.482225 \nL 146.782882 111.372651 \nL 146.856695 111.29047 \nL 146.962143 111.208289 \nL 147.004322 111.098714 \nL 147.236306 110.989139 \nL 147.341754 110.797384 \nL 147.521015 110.687809 \nL 147.594828 110.605628 \nL 147.921715 110.496053 \nL 147.921715 110.46866 \nL 148.227513 110.386479 \nL 148.322415 110.167329 \nL 148.427863 110.057754 \nL 148.53331 109.920786 \nL 148.680937 109.811211 \nL 148.744205 109.72903 \nL 148.912921 109.646849 \nL 149.018369 109.455094 \nL 149.102727 109.345519 \nL 149.17654 109.235944 \nL 149.334711 109.12637 \nL 149.440159 108.989401 \nL 149.566696 108.879827 \nL 149.566696 108.825039 \nL 149.840859 108.742858 \nL 149.946307 108.578496 \nL 150.072844 108.468922 \nL 150.115023 108.386741 \nL 150.389186 108.277166 \nL 150.452455 108.222379 \nL 150.652805 108.112804 \nL 150.758252 107.975836 \nL 150.958603 107.893655 \nL 151.042961 107.78408 \nL 151.158953 107.701899 \nL 151.253856 107.482749 \nL 151.50693 107.373175 \nL 151.612377 107.181419 \nL 151.728369 107.071844 \nL 151.749459 106.934876 \nL 151.949809 106.825301 \nL 152.055257 106.633546 \nL 152.10798 106.551365 \nL 152.202883 106.387003 \nL 152.297786 106.332215 \nL 152.403233 106.14046 \nL 152.593039 106.030885 \nL 152.698486 105.893917 \nL 152.814479 105.811736 \nL 152.835568 105.729555 \nL 153.046463 105.647374 \nL 153.130821 105.455618 \nL 153.225724 105.346043 \nL 153.310082 105.263862 \nL 153.531521 105.154287 \nL 153.584245 105.017319 \nL 153.805685 104.907744 \nL 153.900588 104.79817 \nL 154.027125 104.688595 \nL 154.090393 104.57902 \nL 154.354012 104.524233 \nL 154.427825 104.332477 \nL 154.543817 104.250296 \nL 154.628175 104.058541 \nL 154.891794 103.948966 \nL 154.976152 103.866785 \nL 155.208137 103.75721 \nL 155.229226 103.702423 \nL 155.366308 103.592848 \nL 155.461211 103.45588 \nL 155.535024 103.373699 \nL 155.598292 103.209337 \nL 155.809187 103.099762 \nL 155.883001 102.990187 \nL 156.072806 102.880613 \nL 156.114985 102.798432 \nL 156.294246 102.716251 \nL 156.389148 102.606676 \nL 156.600043 102.497101 \nL 156.705491 102.277952 \nL 156.905841 102.168377 \nL 157.011289 102.004015 \nL 157.085102 101.894441 \nL 157.180005 101.702685 \nL 157.36981 101.620504 \nL 157.475258 101.483536 \nL 157.665063 101.373961 \nL 157.770511 101.29178 \nL 157.897048 101.182205 \nL 157.949771 101.045237 \nL 158.129032 100.935662 \nL 158.150122 100.880875 \nL 158.455919 100.7713 \nL 158.561367 100.689119 \nL 158.63518 100.606938 \nL 158.708993 100.524757 \nL 158.877709 100.415182 \nL 158.983157 100.333001 \nL 159.067515 100.223427 \nL 159.141328 100.168639 \nL 159.362768 100.059065 \nL 159.426036 99.894703 \nL 159.615842 99.785128 \nL 159.710744 99.702947 \nL 159.847826 99.593372 \nL 159.942729 99.456404 \nL 160.174713 99.346829 \nL 160.280161 99.209861 \nL 160.364519 99.12768 \nL 160.459422 99.018105 \nL 160.628138 98.908531 \nL 160.72304 98.853743 \nL 160.923391 98.744169 \nL 161.028838 98.661988 \nL 161.408449 98.552413 \nL 161.503352 98.388051 \nL 161.756426 98.278476 \nL 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_roc_curve(fpr, tpr)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### 随机森林模型"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(random_state=42)\n",
    "y_probas_forest = cross_val_predict(forest_clf, X_train_pca, y_train_norm, cv=5 ,method=\"predict_proba\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores_forest = y_probas_forest[:, 1]\n",
    "fpr_forest, tpr_forest, threshold_forest = roc_curve(y_train_norm, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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179.28156 \nL 82.744621 179.171985 \nL 82.839524 179.035017 \nL 83.103143 178.98023 \nL 83.198045 178.788474 \nL 83.292948 178.678899 \nL 83.356217 178.432356 \nL 83.493298 178.322782 \nL 83.535477 178.131026 \nL 83.756917 178.021451 \nL 83.862365 177.774908 \nL 84.094349 177.665334 \nL 84.189252 177.555759 \nL 84.294699 177.473578 \nL 84.400147 177.227035 \nL 84.516139 177.11746 \nL 84.621586 176.870917 \nL 84.663765 176.843523 \nL 84.758668 176.624374 \nL 84.89575 176.514799 \nL 84.948474 176.460012 \nL 85.138279 176.350437 \nL 85.243727 176.049107 \nL 85.422987 175.939532 \nL 85.528435 175.720383 \nL 85.644427 175.610808 \nL 85.749875 175.446446 \nL 85.823688 175.364265 \nL 85.908046 175.145116 \nL 86.013493 175.035541 \nL 86.108396 174.95336 \nL 86.213844 174.898573 \nL 86.308746 174.65203 \nL 86.498552 174.542455 \nL 86.593455 174.268518 \nL 86.646178 174.158944 \nL 86.751626 173.857613 \nL 86.857073 173.748039 \nL 86.930887 173.638464 \nL 87.089058 173.528889 \nL 87.194505 173.254953 \nL 87.268319 173.145378 \nL 87.373766 173.00841 \nL 87.42649 172.926229 \nL 87.489758 172.734473 \nL 87.658474 172.652292 \nL 87.742832 172.350961 \nL 87.922093 172.26878 \nL 87.995906 171.994844 \nL 88.175167 171.885269 \nL 88.24898 171.693513 \nL 88.386062 171.611332 \nL 88.491509 171.14564 \nL 88.649681 171.036065 \nL 88.744583 170.981278 \nL 88.850031 170.899097 \nL 88.955478 170.597767 \nL 89.018747 170.488192 \nL 89.103105 170.241649 \nL 89.29291 170.132074 \nL 89.398358 169.912925 \nL 89.482716 169.885531 \nL 89.588163 169.584201 \nL 89.7147 169.50202 \nL 89.767424 169.365051 \nL 89.893961 169.255477 \nL 89.967774 168.899359 \nL 90.147035 168.789784 \nL 90.252482 168.488454 \nL 90.326296 168.378879 \nL 90.389564 168.132336 \nL 90.463377 168.022762 \nL 90.505556 167.967974 \nL 90.737541 167.885793 \nL 90.832444 167.721431 \nL 90.916802 167.611856 \nL 90.927346 167.529675 \nL 91.127697 167.420101 \nL 91.233144 167.200951 \nL 91.40186 167.091377 \nL 91.475673 166.954408 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99.753301 156.353058 \nL 99.858749 155.942153 \nL 100.01692 155.832578 \nL 100.101278 155.69561 \nL 100.269994 155.586035 \nL 100.354352 155.394279 \nL 100.470344 155.312098 \nL 100.533613 155.038162 \nL 100.66015 154.928587 \nL 100.765597 154.65465 \nL 100.871045 154.545076 \nL 100.965948 154.435501 \nL 101.113574 154.35332 \nL 101.197932 154.216352 \nL 101.429916 154.106777 \nL 101.514274 153.942415 \nL 101.756804 153.83284 \nL 101.862251 153.421935 \nL 101.936064 153.31236 \nL 102.020422 153.202786 \nL 102.12587 153.093211 \nL 102.220773 152.901455 \nL 102.32622 152.819274 \nL 102.431668 152.627519 \nL 102.54766 152.545338 \nL 102.600384 152.435763 \nL 102.800734 152.326188 \nL 102.885092 152.18922 \nL 102.958905 152.079645 \nL 103.064353 151.915283 \nL 103.138166 151.860496 \nL 103.243613 151.586559 \nL 103.349061 151.476984 \nL 103.401785 151.285229 \nL 103.654859 151.175654 \nL 103.760306 150.983898 \nL 104.087193 150.874324 \nL 104.192641 150.764749 \nL 104.245364 150.655174 \nL 104.340267 150.518206 \nL 104.487894 150.408631 \nL 104.582796 150.216876 \nL 104.730423 150.107301 \nL 104.804236 149.805971 \nL 105.067855 149.696396 \nL 105.131123 149.641609 \nL 105.289295 149.559428 \nL 105.394742 149.367672 \nL 105.436921 149.312884 \nL 105.531824 149.093735 \nL 105.616182 149.011554 \nL 105.721629 148.710224 \nL 105.974703 148.600649 \nL 105.985248 148.491074 \nL 106.175054 148.436287 \nL 106.269956 148.217138 \nL 106.364859 148.107563 \nL 106.438672 147.943201 \nL 106.617933 147.833626 \nL 106.702291 147.778839 \nL 106.797194 147.669264 \nL 106.871007 147.477509 \nL 106.955365 147.422721 \nL 107.050268 147.313147 \nL 107.208439 147.203572 \nL 107.303342 146.984422 \nL 107.419334 146.874848 \nL 107.514237 146.737879 \nL 107.609139 146.628305 \nL 107.714587 146.409155 \nL 107.80949 146.299581 \nL 107.914937 146.2174 \nL 107.98875 146.107825 \nL 108.094198 145.99825 \nL 108.189101 145.888676 \nL 108.294548 145.69692 \nL 108.399996 145.587345 \nL 108.41054 145.505164 \nL 108.800696 145.39559 \nL 108.906144 145.149047 \nL 109.022136 145.039472 \nL 109.117039 144.87511 \nL 109.148673 144.87511 \nL 109.25412 144.601173 \nL 109.412291 144.491598 \nL 109.443926 144.436811 \nL 109.549373 144.382024 \nL 109.644276 144.0533 \nL 109.728634 143.943725 \nL 109.802447 143.751969 \nL 109.89735 143.642395 \nL 110.002797 143.478033 \nL 110.066066 143.450639 \nL 110.160969 143.258883 \nL 110.213692 143.149309 \nL 110.31914 142.984947 \nL 110.466766 142.902766 \nL 110.51949 142.738404 \nL 110.793654 142.71101 \nL 110.899101 142.464467 \nL 111.015093 142.354892 \nL 111.099451 142.19053 \nL 111.225988 142.080955 \nL 111.310346 142.026168 \nL 111.531786 141.916593 \nL 111.58451 141.752231 \nL 111.763771 141.642657 \nL 111.869218 141.396114 \nL 111.953576 141.286539 \nL 112.059024 141.06739 \nL 112.291008 140.957815 \nL 112.375366 140.711272 \nL 112.554627 140.601697 \nL 112.660074 140.437335 \nL 112.754977 140.327761 \nL 112.860424 140.218186 \nL 112.976417 140.108611 \nL 113.081864 139.971643 \nL 113.124043 139.889462 \nL 113.187312 139.670312 \nL 120.273383 135.259931 \nL 120.273383 135.232538 \nL 120.442099 135.17775 \nL 120.526457 135.040782 \nL 120.705718 134.958601 \nL 120.811165 134.712058 \nL 120.958792 134.602483 \nL 121.053694 134.492909 \nL 121.148597 134.383334 \nL 121.2435 134.246366 \nL 121.412216 134.136791 \nL 121.486029 133.945035 \nL 121.844551 133.835461 \nL 121.949998 133.643705 \nL 122.118714 133.53413 \nL 122.160893 133.369768 \nL 122.319064 133.287587 \nL 122.424512 133.150619 \nL 122.50887 133.041044 \nL 122.551049 132.931469 \nL 122.783033 132.849288 \nL 122.888481 132.684926 \nL 123.13101 132.602745 \nL 123.225913 132.41099 \nL 123.33136 132.301415 \nL 123.394629 132.19184 \nL 123.637158 132.082266 \nL 123.679337 132.000085 \nL 123.858598 131.89051 \nL 123.942956 131.835723 \nL 124.058948 131.726148 \nL 124.164395 131.479605 \nL 124.322567 131.37003 \nL 124.417469 131.260456 \nL 124.533462 131.150881 \nL 124.586185 131.013913 \nL 124.849804 130.904338 \nL 124.902528 130.794763 \nL 125.092333 130.685188 \nL 125.187236 130.520826 \nL 125.324318 130.438645 \nL 125.355952 130.356464 \nL 125.566847 130.24689 \nL 125.672295 130.109921 \nL 125.8621 130.02774 \nL 125.967547 129.699016 \nL 126.051905 129.644229 \nL 126.157353 129.452473 \nL 126.368248 129.342899 \nL 126.463151 129.20593 \nL 126.610777 129.096356 \nL 126.631867 129.014175 \nL 126.937664 128.9046 \nL 127.032567 128.712844 \nL 127.222373 128.60327 \nL 127.285641 128.356726 \nL 127.443812 128.247152 \nL 127.538715 128.137577 \nL 127.686342 128.028002 \nL 127.781244 127.808853 \nL 128.118676 127.699278 \nL 128.224124 127.534916 \nL 128.350661 127.425342 \nL 128.403385 127.343161 \nL 128.59319 127.233586 \nL 128.667003 127.151405 \nL 128.962256 127.04183 \nL 129.046614 126.795287 \nL 129.21533 126.685713 \nL 129.299688 126.521351 \nL 129.478949 126.411776 \nL 129.552762 126.329595 \nL 129.647665 126.247414 \nL 129.753112 126.055658 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156.600043 102.497101 \nL 156.705491 102.277952 \nL 156.905841 102.168377 \nL 157.011289 102.004015 \nL 157.085102 101.894441 \nL 157.180005 101.702685 \nL 157.36981 101.620504 \nL 157.475258 101.483536 \nL 157.665063 101.373961 \nL 157.770511 101.29178 \nL 157.897048 101.182205 \nL 157.949771 101.045237 \nL 158.129032 100.935662 \nL 158.150122 100.880875 \nL 158.455919 100.7713 \nL 158.561367 100.689119 \nL 158.63518 100.606938 \nL 158.708993 100.524757 \nL 158.877709 100.415182 \nL 158.983157 100.333001 \nL 159.067515 100.223427 \nL 159.141328 100.168639 \nL 159.362768 100.059065 \nL 159.426036 99.894703 \nL 159.615842 99.785128 \nL 159.710744 99.702947 \nL 159.847826 99.593372 \nL 159.942729 99.456404 \nL 160.174713 99.346829 \nL 160.280161 99.209861 \nL 160.364519 99.12768 \nL 160.459422 99.018105 \nL 160.628138 98.908531 \nL 160.72304 98.853743 \nL 160.923391 98.744169 \nL 161.028838 98.661988 \nL 161.408449 98.552413 \nL 161.503352 98.388051 \nL 161.756426 98.278476 \nL 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188.213201 80.308228 \nL 188.329193 80.226047 \nL 188.434641 80.061685 \nL 188.645536 79.95211 \nL 188.750983 79.787748 \nL 189.056781 79.678174 \nL 189.162229 79.459024 \nL 189.278221 79.34945 \nL 189.383668 79.267269 \nL 189.636742 79.157694 \nL 189.74219 79.075513 \nL 189.847637 78.993332 \nL 189.92145 78.911151 \nL 190.058532 78.82897 \nL 190.058532 78.774183 \nL 190.448688 78.664608 \nL 190.543591 78.555033 \nL 191.00756 78.445459 \nL 191.039194 78.390671 \nL 191.482073 78.281097 \nL 191.587521 78.198916 \nL 191.798416 78.089341 \nL 191.903863 77.979766 \nL 191.998766 77.870191 \nL 191.998766 77.842798 \nL 192.272929 77.733223 \nL 192.378377 77.596255 \nL 192.589272 77.48668 \nL 192.684175 77.267531 \nL 192.958338 77.157956 \nL 193.042696 76.993594 \nL 193.190323 76.884019 \nL 193.190323 76.856626 \nL 193.285225 76.856626 \nL 193.696471 76.747051 \nL 193.749194 76.66487 \nL 193.886276 76.555295 \nL 193.928455 76.473114 \nL 194.13935 76.36354 \nL 194.149895 76.308752 \nL 194.445148 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66.940117 \nL 209.292154 66.830542 \nL 209.387057 66.66618 \nL 209.661221 66.556606 \nL 209.671765 66.501818 \nL 210.209548 66.419637 \nL 210.251727 66.282669 \nL 210.473166 66.173094 \nL 210.536435 66.118307 \nL 210.831688 66.036126 \nL 210.905501 65.84437 \nL 211.190209 65.734795 \nL 211.232388 65.597827 \nL 211.485462 65.488252 \nL 211.559275 65.406071 \nL 211.685812 65.296497 \nL 211.77017 65.132135 \nL 211.928342 65.02256 \nL 212.033789 64.940379 \nL 212.339587 64.830804 \nL 212.445034 64.748623 \nL 212.592661 64.666442 \nL 212.592661 64.611655 \nL 212.982817 64.50208 \nL 212.982817 64.447293 \nL 213.341338 64.337718 \nL 213.425696 64.228144 \nL 213.615502 64.118569 \nL 213.689315 64.063781 \nL 213.889665 63.954207 \nL 213.984568 63.789845 \nL 214.30091 63.68027 \nL 214.353634 63.598089 \nL 214.543439 63.488514 \nL 214.596163 63.406333 \nL 215.028498 63.296759 \nL 215.091766 63.241971 \nL 215.471377 63.132397 \nL 215.555735 62.968035 \nL 215.745541 62.85846 \nL 215.756086 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(fpr, tpr, \"b:\", label=\"SGD\")\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.9360270494767724"
      ]
     },
     "metadata": {},
     "execution_count": 162
    }
   ],
   "source": [
    "roc_auc_score(y_train_norm, y_scores_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = (y_scores_forest>=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[28814,    50],\n",
       "       [ 3491,  3725]])"
      ]
     },
     "metadata": {},
     "execution_count": 164
    }
   ],
   "source": [
    "confusion_matrix(y_train_norm, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "0.9867549668874173"
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     },
     "metadata": {},
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   ],
   "source": [
    "precision_score(y_train_norm, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.5162139689578714"
      ]
     },
     "metadata": {},
     "execution_count": 166
    }
   ],
   "source": [
    "recall_score(y_train_norm, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.6778273132563005"
      ]
     },
     "metadata": {},
     "execution_count": 167
    }
   ],
   "source": [
    "f1_score(y_train_norm, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "----training classifier for MI----\n",
      "accuracy: 0.904\n",
      "precision: 0.993\n",
      "recall: 0.535\n",
      "f1-score: 0.695\n",
      "roc score: 0.950\n",
      "----training classifier for STTC----\n",
      "accuracy: 0.899\n",
      "precision: 0.987\n",
      "recall: 0.516\n",
      "f1-score: 0.678\n",
      "roc score: 0.936\n",
      "----training classifier for CD----\n",
      "accuracy: 0.905\n",
      "precision: 0.911\n",
      "recall: 0.587\n",
      "f1-score: 0.714\n",
      "roc score: 0.914\n",
      "----training classifier for HYP----\n",
      "accuracy: 0.940\n",
      "precision: 0.990\n",
      "recall: 0.720\n",
      "f1-score: 0.833\n",
      "roc score: 0.963\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_scores_forest_all = []\n",
    "for train_class in range(1,5):\n",
    "    print(\"----training classifier for %s----\"%(num2class[train_class]))\n",
    "    y_train_norm = (os_label == train_class)\n",
    "\n",
    "    forest_clf = RandomForestClassifier(random_state=42)\n",
    "    y_probas_forest = cross_val_predict(forest_clf, X_train_pca, y_train_norm, cv=5 ,method=\"predict_proba\")\n",
    "    y_scores_forest = y_probas_forest[:, 1]\n",
    "    y_train_pred = (y_scores_forest>=0.5)\n",
    "    y_scores_forest_all.append(y_scores_forest)\n",
    "\n",
    "    print(\"accuracy: %.3f\" % np.mean(cross_val_score(forest_clf, X_train_pca, y_train_norm, cv=5, scoring=\"accuracy\")))\n",
    "    print(\"precision: %.3f\" % precision_score(y_train_norm, y_train_pred))\n",
    "    print(\"recall: %.3f\" % recall_score(y_train_norm, y_train_pred))\n",
    "    print(\"f1-score: %.3f\" % f1_score(y_train_norm, y_train_pred))\n",
    "    print(\"roc score: %.3f\" % roc_auc_score(y_train_norm, y_scores_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb2df1851d0>"
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     },
     "metadata": {},
     "execution_count": 224
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "line = [\"\",\"r-\", \"b-.\", \"r:\", \"b--\"]\n",
    "plt.figure()\n",
    "plt.rcParams['figure.figsize'] = (5.0, 5.0)\n",
    "\n",
    "for i in range(3):\n",
    "\n",
    "    y_train_norm = (os_label == i + 1)\n",
    "    fpr_forest, tpr_forest, threshold_forest = roc_curve(y_train_norm, y_scores_forest_all[i])\n",
    "\n",
    "    plt.plot(fpr_forest, tpr_forest, line[i+1], label=num2class[i+1])\n",
    "\n",
    "y_train_norm = (os_label == 4)\n",
    "fpr_forest, tpr_forest, threshold_forest = roc_curve(y_train_norm, y_scores_forest_all[3])\n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"HYP\")\n",
    "plt.legend(loc=\"lower right\")"
   ]
  }
 ]
}